Method of simulating deformable object using geometrically motivated model

ABSTRACT

A method of simulating a deformable object comprises modeling deformable elasticity for the object by defining an actual shape and a goal shape and pulling points in the goal shape towards corresponding points in the goal shape.

This application claims the benefit of U.S. Provisional Application No.60/678,815 filed on May 9, 2005.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Embodiments of the present invention relate generally to methods ofsimulating deformable objects. More particularly, embodiments of theinvention relate to methods of simulating deformable objects using ageometrically motivated underlying model.

2. Description of Related Art

Realistic computer simulations of everyday objects such as clothing,plastics, elastic materials, and articulated joints can be extremelydifficult to generate due to the complex ways in which these objectstend to deform in real life. Simulations of such “deformable objects”must generally take into account both complicated geometric features andmaterial properties of the objects.

One common technique used to simulate deformable objects first creates avirtual model of an object (e.g., a mesh or point cloud) and thenapplies simulated physical forces such as tension, friction, gravity,pressure, etc., to the discrete points of the virtual model. Suchvirtual models have been used to represent a wide variety of materialsunder different conditions. For example, researchers have developedvirtual models for clothing, plastic, rubber, and so on. In addition,researchers have also developed virtual models to simulate complex,unique behaviors of these objects, such as fracturing and melting.

Some of the more common approaches to simulating deformable objectsinvolve finite difference methods, mass-spring systems, boundary elementmethods, finite element methods, and implicit surfaces and mesh-freeparticle systems.

Despite the number of methods that have been developed for simulatingdeformable objects, such methods are rarely incorporated intoapplications like computer games. For example, a few contemporary gamesincorporate cloth models with simple geometries; however, in general,most applications running on Personal Computers (PCs) or game consolestend to model only rigid bodies or objects. Indeed, multiple rigidobjects are sometimes combined to imitate the movement of a deformableobject, but true deformable object simulations are rare.

One reason why deformable object simulations are rarely used in anapplication such as computer games is that stable simulations aregenerally too inefficient to satisfy the real-time demands of theapplication. For instance, conventional simulation methods often rely onimplicit numerical integration to update the locations of discretepoints in a virtual model. Implicit numerical integration techniques canprovide a stable simulation, but they are too computationally expensiveto process large and complicated physical models in real-time. Otherphysical modeling approaches rely on explicit numerical integration,which is more efficient, but it cannot guarantee a stable simulation.

The term “stability” here refers to a process's tendency to respond in areasonable way to minor deviations in its inputs. For instance, implicitnumerical integration techniques produce accurate simulations as variousinput parameters are varied, such as the mass of simulated points, thetimestep of the integration, and so on. In contrast, explicit numericalintegration techniques may produce simulations where the overall energyof a system erroneously increases by simply varying the timestep of theintegration, the mass of simulated points, or the stiffness of asimulated object. As a result, explicit numerical integration techniquescan produce highly unrealistic simulation results.

Various approaches have been developed to improve the efficiency ofstable simulation techniques. For example, robust integration schemeswith large timesteps and multi-resolution models have been developed. Inaddition, modal analysis approaches, which trade accuracy forefficiency, have also been developed. Furthermore, methods incorporatingpre-computed state space dynamics and pre-computed impulse responsefunctions have also been used to improve the efficiency of simulations.Finally, dynamic models derived from global geometric deformations ofsolid primitives such as spheres, cylinders, cones, or super quadricshave also been introduced to improve the efficiency of stable simulationtechniques.

FIG. 1 is a diagram illustrating one way in which instability arises indeformable object simulations using explicit numerical integration. InFIG. 1, a simple one dimensional deformable object is modeled as amass-spring system. Like many physically motivated deformable objectsimulations, the mass-spring system relies on Newton's second law ofmotion. According to Newton's second law of motion, the acceleration ofan object produced by a net force is directly proportional to themagnitude of the net force in the direction of the net force, andinversely proportional to the mass of the object. In deformable objects,at least part of the net force at any point is created by displacementof the point from an equilibrium position. The displacement of a pointfrom its equilibrium position creates potential energy, i.e.,“deformation energy”, causing the point to be pulled toward theequilibrium position.

Referring to FIG. 1A, a mass-spring system 100 comprises a spring 101with a resting length l₀, and two point masses 102 and 103 both havingmass “m”. Point mass 102 is fixed at an origin and point mass 103 islocated at x(t) at an initial time “t”. At initial time “t”, the amountof force on point mass 103 is defined by the spring equationf=−k(x(t)−l₀), where “K” is the spring constant, or “stiffness”, ofspring 101. Conceptually, the spring equation indicates that point mass103 is pulled toward an equilibrium position where x=l₀.

The location of point mass 103 is updated by a modified Eulerintegration scheme after a timestep “h”. According to the modified Eulerintegration scheme, the velocity “v” and position “x” of point mass 103at time “t+h” are computed using the following equations (1) and (2):$\begin{matrix}{{v\left( {t + h} \right)} = {{v(t)} + {h\frac{- {k\left( {{x(t)} - l_{0}} \right)}}{m}}}} & (1) \\{{x\left( {t + h} \right)} = {{x(t)} + {{hv}\left( {t + h} \right)}}} & (2)\end{matrix}$Equation (1) uses an explicit Euler step and equation (2) uses animplicit Euler step.

Equations (1) and (2) can be represented as a system matrix “M”multiplied by a state vector [v(t), x(t)]^(T), i.e.,[v(t+h)/x(t+h)]=E[v(t)/x(t)]+1/m[kl₀h²/kl₀h], where system matrix “E” isdefined by the following equation (3): $\begin{matrix}{E = {\begin{bmatrix}1 & {- \frac{kh}{m}} \\h & {1 - \frac{h^{2}k}{m}}\end{bmatrix}.}} & (3)\end{matrix}$System matrix “E” has eigenvalues e₀ and e₁ represented by the followingequations (4) and (5): $\begin{matrix}{e_{0} = {1 - {\frac{1}{2m}\left( {{h^{2}k} - \sqrt{{{- 4}{mh}^{2}k} + {h^{4}k^{2}}}} \right)}}} & (4) \\{e_{1} = {1 - {\frac{1}{2m}{\left( {{h^{2}k} + \sqrt{{{- 4}{mh}^{2}k} + {h^{4}k^{2}}}} \right).}}}} & (5)\end{matrix}$Since system matrix “E” represents a discrete system, the spectralradius of system matrix “E”, i.e., the maximum magnitude of eigenvaluese₀ and e₁, must not be larger than one (1) to ensure stability of thediscrete system. The magnitude of eigenvalue e₀ converges to 1 with|e₀|<1 for h²k→∞. However, the magnitude of e₁ is only smaller than onewhere timestep “h” is less than $2{\sqrt{\frac{m}{k}}.}$Where timestep “h” is greater than ${2\sqrt{\frac{m}{k}}},$the system is unstable. Accordingly, the integration scheme involvingequations (1) and (2) is only conditionally stable.

To further illustrate the instability of the discrete system representedby system matrix “E”, FIG. 1B shows the result of performing anintegration step starting with v(t)=0. The integration step moves pointmass 103 by a distance${\Delta\quad x} = {{- \frac{h^{2}k}{m}}{\left( {{x(t)} - l_{0}} \right).}}$Where timestep “h” or stiffness “K” are too large or mass “m” is toosmall, point mass 103 overshoots equilibrium position l₀, by a distancegreater than the distance between x(t) and l₀. In other words,|x(t+h)−l₀|>|x(t)−l₀|. As a result, the potential energy of system 100increases after timestep “h”. Since system 100 had zero kinetic energyat time “f”, the overall (i.e., kinetic plus potential) energy of thesystem is erroneously increased after timestep “h”.

In general, the stability problem of explicit integration schemes can bestated as follows: elastic forces are negative gradients of elasticenergy. As such, elastic forces point towards equilibrium positions.Explicit schemes may inaccurately scale the elastic forces whencomputing the displacements of points, causing the points to overshootequilibrium positions so much that they increase the deformation and theenergy of the system instead of preserving or decreasing the deformationenergy, which is required for stability.

One way to address the overshoot problem is to limit the displacement ofpoints such that they never overshoot their respective equilibriumpositions. For instance, in the one-dimensional spring example of FIG.1, the movement of point mass 103 could be restricted from passing theequilibrium position x=l₀. One problem with this approach is that formany types of physical models, equilibrium positions are not readilydefined for all points. For example, it is difficult to defineequilibrium positions in solid finite elements or geometrically complexmeshes.

SUMMARY OF THE INVENTION

According to one embodiment of the invention, a method of modeling adeformable object comprises modeling deformable elasticity for theobject by pulling a deformed shape towards a defined goal shape.

According to another embodiment of the invention, a method of simulatinga deformable object comprises defining positions and velocities for aplurality of points in a deformed shape, and updating the positions andvelocities according to the positions of points in a goal shape.

According to still another embodiment of the invention, a method ofdescribing object deformation in a simulation comprises defining elasticforces associated with the object deformation in proportion to distancesbetween points in a deformed shape and points in a goal shape, resolvingthe object deformation toward an equilibrium using an explicitintegration scheme, and resolving the explicit integration scheme bymatching on a point by point basis an original shape and the deformedshape and then pulling points corresponding to the deformed shapetowards a corresponding points in the goal shape.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is described below in relation to several embodimentsillustrated in the accompanying drawings. Throughout the drawings likereference numbers indicate like exemplary elements, components, orsteps. In the drawings:

FIGS. 1A and 1B are diagrams of a conventional one dimensionalmass-spring system used to simulate a deformable object;

FIG. 2 is a diagram illustrating a simulation of a two dimensionaldeformable object according to one embodiment of the present invention;and,

FIG. 3 is a flowchart illustrating a method of simulating a deformableobject according to an embodiment of the present invention.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

Embodiments of the present invention provide various methods of modelingand simulating deformable objects. These methods can be readily appliedto a wide range of computer-related applications such as scientificvisualization, computer graphics, computer animated films, and videogames, to name but a few.

Selected embodiments of the invention are particularly well-suited forapplications requiring a high level of computational efficiency. Forinstance, some embodiments of the invention are capable of performingreal-time simulations on deformable objects with complicated geometriesand/or material properties using only a small portion of a computer'sdata processing bandwidth.

Video games are one application that requires a high level ofcomputational efficiency. For example, state-of-the-art video games tendto incorporate a variety of realistic effects, such as characters andobjects that interact with their environment in real time as if governedby the laws of physics. Such characters and objects are often formed bydeformable objects including, for example, clothing, bendable orstretchable materials, and so on.

A deformable object simulation according to embodiments of the inventionis typically performed by running a software application on acomputational platform including at least one microprocessor and memory.The term “run” here describes any process in which a hardware resourceassociated with a computational platform performs an operation under thedirection (directly or indirectly) of a software resource.

To simulate a deformable object, the software application receivesgeometric data and physics data defining a configuration of thedeformable object and any external forces acting on the object. The“configuration” of a deformable object is broadly defined as adescription of all physical attributes of the object itself, including,for example, the locations and masses of discrete elements constitutingthe object (e.g., points, surfaces, etc.), any connectivity and movementof those discrete elements, and so forth.

The software application simulates the deformable object by updating theobject's configuration based on internal forces of the object such aselastic tension, the movement of discrete elements comprising theobject, and any external forces acting on the object such as gravity,pressure, or friction, or impact forces from collisions with otherobjects.

The computational platform typically comprises one or more centralprocessing units (CPUs) and one or more memories. The one or morememories store the software application and loads it into the one ormore CPUs for execution.

A deformable object simulation may also be performed with more than onesoftware application. For example, the simulation could be performed bytwo software applications running in two execution threads on a singleCPU, on two different processor cores, or two different CPUs. Where thesimulation is performed by two software applications, one of theapplications generally comprises a main application defining thegeometric and physics data, and the other application generallycomprises a physics application running in parallel with the mainapplication and updating the geometric and physics data.

Various computational platforms capable of performing such a simulationare disclosed, for example, in U.S. patent applications with Ser. Nos.10/715,459 and 10/715,440 filed Nov. 19, 2003, U.S. Ser. No. 10/815,721filed Apr. 2, 2004, U.S. Ser. 10/839,155 filed May 6, 2004, U.S. Ser.No. 10/982,791 filed Nov. 8, 2004, and U.S. Ser. No. 10/988,588 filedNov. 16, 2004. The subject matter of these commonly-assigned, co-pendingpatent applications is hereby incorporated by reference.

In this description, the term “deformable object” refers broadly to anycollection of data capable of representing an object comprising elementsthat can be arranged with varying spatial relationships. For example, adeformable object could comprise a mesh, a surface, or a set of points.A deformable object generally further comprises parameters related tothe way the object tends to deform. For example, the deformable objectmay comprise a goal state for each of its elements, and a stiffnessparameter specifying how easily each element approaches its goal state.

FIG. 2 is a diagram illustrating a method of modeling and simulating adeformable object according to one embodiment of the invention. Forsimplicity of explanation, the method of FIG. 2 is described in relationto a two-dimensional (2D) object. However, the method can be readilyapplied to objects expressed in higher (e.g., 3D) or lower (e.g., 1D)dimensions.

Referring to FIG. 2, a deformable object 200 is modeled by an “actualshape” 202 corresponding to a deformed state of deformable object 200,and a “goal shape” 203 corresponding to a non-deformed state ofdeformable object 200. Actual shape 202 comprises four “actual points”x₁, x₂, x₃, and x₄, and goal shape 203 comprises four “goal points” g₁,g₂, g₃, and g₄ corresponding to respective actual points x₁, x₂, x₃, andx₄. Actual points x₁, x₂, x₃, and x₄ are characterized by respectivemasses “m₁”, “m₂”, “m₃”, and “m₄”.

Goal shape 203 is defined by matching an “original shape” 201 comprisingfour “original points” x₁ ⁰, x₂ ⁰, x₃ ⁰, and x₄ ⁰ to correspondingactual points x₁, x₂, x₃, and x₄ in actual shape 202. In other words,goal shape 203 is a matched version of original shape 201. The term“match” here refers to a process of transformation applied to originalshape 201 such that goal shape 203 will approximate actual shape 202. Aswill be explained, such a transformation can comprise, for example, alinear transformation, a higher order (e.g., quadratic) transformation,or some combination thereof. Once goal shape 203 is defined, deformableelasticity in deformable object 200 is then modeled by pulling actualpoints x₁, x₂, x₃, and x₄ toward corresponding goal points g₁, g₂, g₃,and g₄, as indicated by arrows in FIG. 2.

In this written description, the notations x_(i) ⁰, x_(i), and g_(i) areused to refer to points and also to locations of the points. Forexample, the location of an actual point x_(i) is denoted as simplyx_(i).

One way to match original shape 201 to actual shape 202 is by rotatingand translating original shape 201. The amount of rotation andtranslation can be determined by minimizing some distance between pointsin goal shape 203 and corresponding points in actual shape 202. Forexample, the translation and rotation could be chosen to minimize aweighted least squares distance between corresponding points in goalshape 203 and actual shape 202. Correspondences between points inoriginal shape 201 and actual shape 202 are generally defined a priori,for example, when original shape 201 and actual shape 202 are firstdefined.

In FIG. 2, corresponding points are labeled with the like subscripts.For example, original point x₁ ⁰ corresponds to actual and goal pointsx₁ and g₁. Accordingly, the configuration of goal shape 203 can becomputed by finding a rotation matrix R and translation vectors t₀ and tsuch that the following equation (5) is minimized: $\begin{matrix}{\sum\limits_{i}{{w_{i}\left( {{R\left( {x_{i}^{0} - t_{0}} \right)} + t - x_{i}} \right)}^{2}.}} & (5)\end{matrix}$In equation (5), w_(i) represents a mathematical weight associated withoriginal and actual points x_(i) ⁰ and x_(i). Typically, theweight-assigned to each point is the point's mass. Accordingly, tosimplify this written description, weights w_(i) are substituted bymasses “m_(i)” throughout.

Where the mathematical weight w_(i) of each point in deformable object200 is the point's mass “m_(i)”, translation vectors t₀ and t aredefined as respective centers of mass x_(cm) ⁰ and x_(cm) of originalshape 201 and actual shape 202, defined by the following equations (6)and (7): $\begin{matrix}{t_{0} = {x_{c\quad m}^{0} = \frac{\sum\limits_{i}{m_{i}x_{i}^{0}}}{\sum\limits_{i}m_{i}}}} & (6) \\{t = {x_{c\quad m} = \frac{\sum\limits_{i}{m_{i}x_{i}}}{\sum\limits_{i}m_{i}}}} & (7)\end{matrix}$

To compute rotation matrix R, relative locations q_(i) and p_(i) of thepoints in original shape 201 and actual shape 202 are defined asq_(i)=x_(i) ⁰−x_(cm) ⁰ and p_(i)=x_(i)−x_(cm). A linear transformationmatrix “A” minimizing the following equation (8) is then computed:Σ_(i)m_(i)(Aq_(i)−p_(i))².   (8)

The linear transformation matrix “A” that minimizes equation (8) iscomputed by taking the derivative of equation (8) with respect to “A”and setting the derivative to zero. The resulting linear transformationmatrix “A” is computed by the following equation (9): $\begin{matrix}{A = {{\left( {\sum\limits_{i}{m_{i}p_{i}q_{i}^{T}}} \right)\left( {\sum\limits_{i}{m_{i}q_{i}q_{i}^{T}}} \right)^{- 1}} = {A_{pq}{A_{qq}.}}}} & (9)\end{matrix}$

Equation (9) defines a first matrix A_(qq), and a second matrix A_(pq).First matrix A_(qq) is symmetric, and therefore it contains a scalingcomponent, but no rotational component. In contrast, second matrixA_(pq) contains both a symmetric component and a rotational component.The rotational component of second matrix A_(pq) is rotation matrix Rand the symmetric component of second matrix A_(pq) is a symmetricmatrix S. Rotation matrix R and symmetric matrix S can be found bydecomposing second matrix A_(pq) a via polar decomposition, representedby A_(pq)=RS. In the polar decomposition, symmetric matrix S is computedas S=√{square root over (A_(pq) ^(T)A_(pq))} and rotation matrix R iscomputed as R=A_(pq)S⁻¹. Once rotation matrix R is defined, thelocations of goal points g₁, g₂, g₃, and g₄ are computed by thefollowing equation (10):g _(i) =R(x _(i) ⁰ −x _(cm) ⁰)+x _(cm).   (10)

Then, upon computing the locations of goal points g₁, g₂, g₃, and g₄,the locations of actual points x₁, x₂, x₃, and x₄ are updated byintegration according to the following equations (11) and (12):$\begin{matrix}{{v_{i}\left( {t + h} \right)} = {{v_{i}(t)} + {\alpha\frac{{g_{i}(t)} - {x_{i}(t)}}{h}} + {{{hf}_{ext\_ i}(t)}/m_{i}}}} & (11) \\{{x_{i}\left( {t + h} \right)} = {{x_{i}(t)} + {{hv}_{i}\left( {t + h} \right)}}} & (12)\end{matrix}$In equations (11) and (12), the term a represents a “stiffness” ofdeformable object 200. The term α ranges from zero to one, where a valueof α=1 indicates that deformable object 200 is rigid, and a value of α=0indicates that deformable object 200 is highly deformable. The termf_(ext) _(—) _(i)(t) denotes a net external force acting on point “i” ofactual shape 202 at time t.

To illustrate the effect of a on actual shape 202, suppose that α=1,initial velocity v(t)=0, and net external force f_(ext) _(—) _(i)(t)=0.Under these conditions equation (11) evaluates as${{v_{i}\left( {t + h} \right)} = {\alpha\frac{{g_{i}(t)} - {x_{i}(t)}}{h}}},$and equation (12) becomesx_(i)(t+h)=x_(i)(t)+g_(i)(t)−x_(i)(t)=g_(i)(t). In other words, ifdeformable object 200 is rigid, deformable shape 202 tends to approachthe configuration of goal shape 203 very quickly. Where α<1, deformableshape 202 still approaches the configuration of goal shape 203, however,it does so more slowly.

Applying equations (11) and (12) to mass-spring system 100 shown in FIG.1, the velocity and position of point mass 103 are updated according tothe following equation (13): $\begin{matrix}{\begin{bmatrix}{v\left( {t + h} \right)} \\{x\left( {t + h} \right)}\end{bmatrix} = {{\begin{bmatrix}1 & {{- \alpha}/h} \\h & {1 - \alpha}\end{bmatrix}\begin{bmatrix}{v(t)} \\{x(t)}\end{bmatrix}} + {\begin{bmatrix}{\alpha\quad{l_{0}/h}} \\{\alpha\quad l_{0}}\end{bmatrix}.}}} & (13)\end{matrix}$

In equation (13), the term $\begin{bmatrix}1 & {{- \alpha}/h} \\h & {1 - \alpha}\end{bmatrix}\quad$represents a system matrix similar to system matrix “E” in equation (3).However, unlike the eigenvalues of system matrix “E”, the magnitudes ofthe eigenvalues of the term $\begin{bmatrix}1 & {{- \alpha}/h} \\h & {1 - \alpha}\end{bmatrix}\quad$are always one (1), regardless of the respective values of α andtimestep h. IN particular, the eigenvalues of the term $\begin{bmatrix}1 & {{- \alpha}/h} \\h & {1 - \alpha}\end{bmatrix}\quad$are defined as (1−α/2)±√{square root over (α²−4α)}/2. Because theeigenvalues of the term $\begin{bmatrix}1 & {{- \alpha}/h} \\h & {1 - \alpha}\end{bmatrix}\quad$are always equal to one, a simulation of mass-spring system 100 usingequations (11) and (12) is unconditionally stable. Moreover, thesimulation using equations (11) and (12) does not introduce damping intomass-spring system 100.

A three dimensional simulation of a deformable object with no externalforces is also unconditionally stable and free of damping underequations (11) and (12). Moreover, as long as the external forcesapplied to a deformable object are invariant with respect to location,e.g., forces such as gravity, or the external forces are appliedinstantaneously, e.g., collision response forces, the simulation will bealso be unconditionally stable.

FIG. 3 is a flowchart illustrating a method of simulating a deformableobject according to an embodiment of the invention. In the descriptionthat follows, exemplary method steps are denoted by parentheses (XXX).

Referring to FIG. 3, the method comprises defining an actual shapecorresponding to a deformed state of the deformable object (301),defining a goal shape corresponding to a non-deformed state of thedeformable object (302), and updating the location of each point in theactual shape by pulling the point toward a corresponding point in thegoal shape (303).

Positions of actual points in the actual shape are generally defined byevents in a software application, such as initialization and subsequentupdates of the actual shape's configuration. Meanwhile, positions ofgoal points in the goal shape can be computed in a variety of differentways.

According to one embodiment of the present invention, the positions ofgoal points g_(i) in the goal shape are computed by defining an originalshape comprising original points and rotating and translating theoriginal shape to match the actual shape using a translation vector androtation matrix, e.g., as described by equation (10).

The translation vector is generally computed from the respective centersof mass of the original and actual shapes, as described by equations (6)and (7), and the rotation matrix is generally computed by polardecomposition of a linear transformation matrix computed according toequation (9).

In general, center of mass x_(cm) ⁰ in equation (10) and relativelocations q_(i) in equations (8) and (9) are computed before anytimesteps of the simulation are executed. Then, at each timestep of thesimulation, second matrix A_(pq)=Σ_(i)m_(i)p_(i)q_(i) ^(T) is assembled.Where the deformable object being simulated is three dimensional, secondmatrix A_(pq) is a 3×3 matrix.

Second matrix A_(pq) is decomposed into rotation matrix “R” andsymmetric matrix “S” by computing symmetric matrix S as S=√{square rootover (A_(pq) ^(T)A_(pq))} and rotation matrix R as R=A_(pq)S⁻¹. The termS⁻¹, i.e., (√{square root over (A_(pq) ^(T)A_(pq))})⁻¹, is computed bydiagonalizing the symmetric matrix A_(pq) ^(T)A_(pq) using 5-10 Jacobirotations, where the computational complexity of each Jacobi rotation isconstant.

According to another embodiment of the invention, the positions of goalpoints g_(i) are computed by a linear transformation using rotationmatrix “R” and linear transformation matrix “A” according to thefollowing equation (14): $\begin{matrix}{g_{i} = {{\left( {{\beta\frac{A}{\sqrt[3]{\det(A)}}} + {\left( {1 - \beta} \right)R}} \right)\left( {x_{i}^{0} - x_{c\quad m}^{0}} \right)} + {x_{c\quad m}.}}} & (14)\end{matrix}$In equation (14), the term B is a control parameter used to control thelocations of goal points g_(i). Linear transformation matrix “A” isdivided by $\sqrt[3]{\det(A)}$to ensure that the volume of the goal shape relative to the originalshape is conserved by equation (14). Linear transformation matrix “A”,is generally constructed by forming first matrix A_(qq) before anysimulation timesteps are executed and then forming second matrix A_(pq)with each timestep.

One advantage of using equation (14) to compute the positions of goalpoints g_(i) rather than equation (10) is that equation (14) cangenerate goal points g_(i) closer the locations of actual points x_(i).Accordingly, equation (14) is generally better at simulating more highlydeformed, and/or less rigid objects.

Another way to compute goal points g_(i) is by performing a quadratictransformation on the original points. For example, goal points g_(i)can be computed by a quadratic transformation defined by the followingequation (15):g_(i)=[AQM]{tilde over (q)}_(i).   (15)In equation (15), g_(i) ε R³, and {tilde over(q)}=[q_(x),q_(y),q_(z),q_(x) ²,q_(y) ²,q_(z)²,q_(x)q_(y),q_(y)q_(z),q_(z)q_(x)]^(T) ε R⁹. A ε R^(3×3) contains thecoefficients for the linear terms q_(x), q_(y), q_(z), Q ε R^(3×3)contains the coefficients for the purely quadratic terms q_(x) ²,q_(y)²,q_(z) ², and M ε R^(3×3) contains the coefficients for the mixed termsq_(x)q_(y), q_(y)q_(z), q_(z)q_(x). Quadratic transformation matrixÃ=[AQM]ε R^(3×9) preferably minimizes the equation Σ_(i)m_(i) (Ã{tildeover (q)}_(i)−p_(i))², and is computed by the following equation (16):$\begin{matrix}{\overset{\sim}{A} = {{\left( {\sum\limits_{i}{m_{i}p_{i}{\overset{\sim}{q}}_{i}^{T}}} \right)\left( {\sum\limits_{i}{m_{i}{\overset{\sim}{q}}_{i}{\overset{\sim}{q}}_{i}^{T}}} \right)} = {{\overset{\sim}{A}}_{pq}{{\overset{\sim}{A}}_{qq}.}}}} & (16)\end{matrix}$A symmetric matrix Ã_(qq) ε R^(9×9) and vector {tilde over (q)}_(i) inequation (16) can be computed before a simulation begins. Also, thecontrol parameter β can be used with quadratic transformation matrix Ãto further control the positions of the goal points. For example, thegoal points could be generated by the equation g_(i)=[βÃ+(1−β){tildeover (R)}]{tilde over (q)}_(i), where {tilde over (R)} ε R^(3×9)=[R 0 0]instead of using equation (15).

Still another way to compute goal points g_(i) is by dividing actualpoints x_(i) into overlapping clusters and then computing a separatetransformation matrix for each cluster. For example, actual points x_(i)represented by a volumetric mesh can be divided into clusters where eachcluster comprises points adjacent to a common element of the volumetricmesh (e. g. tetrahedron). Alternatively, a mesh can be regularly dividedinto overlapping cubical regions, and then a cluster can be formed byall points in each cubical region.

At each timestep, original points and actual points corresponding toeach cluster are matched to generate goal points g_(i) ^(c)(t), where“i” denotes the index of each point, and “c” denotes a particularcluster. Using goal points g_(i) ^(c)(t) instead of goal pointsg_(i)(t), equation (11) becomes the following equation (17):$\begin{matrix}{{v_{i}\left( {t + h} \right)} = {{v_{i}(t)} + {\alpha\frac{{g_{i}^{c}(t)} - {x_{i}(t)}}{h}} + {{{hf}_{ext\_ i}(t)}/{m_{i}.}}}} & (17)\end{matrix}$

One general problem with the velocity update in equations (11) and (17)is that the behavior of the system is highly dependant on timestep “h”.One way to address the problem is by setting α=h/τ, where τ≦h is a timeconstant.

Not all points of an actual shape or original shape need to beconsidered when computing a transformation matrix to define thelocations of goal points g_(i). For example, where a deformable objectcomprises a large number of points, a subset of the actual points andcorresponding original points defining the deformable object can be usedto generate a transformation matrix. The transformation matrix can thenbe used to transform all of the original points to generate the goalpoints.

The method described in relation to FIG. 3 can also be used to simulateplastic deformable objects. Typically, a plastic deformable object issimulated by representing a deformation state S^(p) for the object. Thedeformation state S^(p) is initialized with the identity matrix “I” andthen updated with each timestep of the simulation.

Deformation state S^(p) is updated based on symmetric matrix S derivedby the polar decomposition of second matrix A_(pq) of equation (9).Symmetric matrix S represents a deformation of the original shape in anunrotated reference frame. Accordingly, where an amount of deformation(i.e., a distance) ∥S−I∥₂ exceeds a threshold value c_(yield), statematrix S^(p) is updated according to the following equation (18):S^(p)←[I+hc_(creep)(S−I)]S^(p).   (18)In equation (18), timestep “h” and the parameter c_(creep) are used tocontrol the plasticity of the deformable object. The plasticity can bebound by testing whether ∥S^(p)−I∥₂ exceeds a threshold value c_(max).Where ∥S^(p)−I∥₂>c_(max), state matrix is set by the following equation(19):S^(p)←I+c_(max)(S^(p)−I)/∥S^(p)−I∥₂.   (19)State matrix S^(p) is incorporated into the simulation of the deformableobject by replacing the definition of q_(i)=x_(i) ⁰−x_(cm) ⁰ in equation(8) with the following equation (20):q _(i) =S ^(p)(x _(i) ⁰ −x _(cm) ⁰).   (20)To ensure that the volume of the deformable object is conservedthroughout the simulation, state matrix S^(p) is divided by$\sqrt[3]{\det\left( S^{p} \right)}$every time it gets updated. Note that each time S^(p) is updated, firstand second matrices A_(qq) and A_(pq) must also be updated.

The foregoing preferred embodiments are teaching examples. Those ofordinary skill in the art will understand that various changes in formand details may be made to the exemplary embodiments without departingfrom the scope of the present invention as defined by the followingclaims.

1. A method of modeling a deformable object, the method comprising:modeling deformable elasticity for the object by pulling a deformedshape towards a defined goal shape.
 2. The method of claim 1, furthercomprising: defining the goal shape using an extended shape matchingtechnique.
 3. The method of claim 2, further comprising: definingrelative positions for points in the deformed shape and an originalshape; and defining the goal shape by computing a transformation betweenthe relative positions of points in the original shape and relativepositions of points in the deformed shape.
 4. The method of claim 3,further comprising: before computing the transformation, applying adeformation state to the relative positions of the points in theoriginal shape.
 5. The method of claim 4, further comprising:initializing the deformation state with the identity matrix; andupdating the deformation state based on a deformation of the originalshape in an unrotated reference frame.
 6. The method of claim 2, whereindefining the goal shape using the extended shape matching techniquecomprises: rotating and translating an original shape to define the goalshape.
 7. The method of claim 6, further comprising: defining a rotationmatrix and a translation vector for rotating and translating theoriginal shape to minimize a weighted least squares difference betweenthe goal shape and the deformed shape.
 8. The method of claim 7, furthercomprising: defining the rotation matrix by performing polardecomposition on a linear transformation matrix; and, defining thetranslation vector by computing respective centers of mass for thedeformed shape and the original shape.
 9. The method of claim 1, furthercomprising: varying the deformable elasticity in accordance with alinear or quadratic mode.
 10. The method of claim 9, wherein varying thedeformable elasticity in accordance with the linear mode comprises:applying a linear transformation to an original shape to define the goalshape.
 11. The method of claim 9, wherein varying the deformableelasticity in accordance with the quadratic mode comprises: applying aquadratic transformation to an original shape to define the goal shape.12. The method of claim 1, further comprising: defining the deformedshape as a plurality of clusters.
 13. The method of claim 12, whereinmodeling deformable elasticity for the object by pulling the deformedshape towards the defined goal shape comprises: pulling data points ineach of the clusters towards data points in corresponding clusterswithin the goal shape.
 14. A method of simulating a deformable object,the method comprising: defining positions and velocities for a pluralityof points in a deformed shape; and, updating the positions andvelocities according to the positions of points in a goal shape.
 15. Themethod of claim 14, wherein updating the positions and velocitiescomprises: defining a velocity of a point in the deformed shape at atime t+h based on a velocity of the point at a time t and a differencebetween a position of a goal point and a position of the point at a timet; and, defining a position of the point at time t+h based on apositioin of the point at time t and the velocity of the point at timet+h.
 16. A method of describing object deformation in a simulation, themethod comprising: defining elastic forces associated with the objectdeformation in proportion to distances between points in a deformedshape and points in a goal shape; resolving the object deformationtoward an equilibrium using an explicit integration scheme; andresolving the explicit integration scheme by matching on a point bypoint basis an original shape and the deformed shape and then pullingpoints corresponding to the deformed shape towards a correspondingpoints in the goal shape.
 17. The method of claim 16, wherein matchingon a point by point basis the original shape and the deformed shapecomprises: selecting a set of points x_(i) ⁰ in the original shape and acorresponding set of points x_(i) in the deformed shape; finding arotation matrix R and a translation vectors t and t₀ to minimize: thesummation for all “i” of (w_(i)(R(x_(i) ⁰−t₀)+t−x_(i))², where w_(i) areweights for each point.
 18. The method of claim 17, further comprising:for each point x_(i) ⁰, defining a location for a corresponding pointg_(i) in the goal shape as g_(i)=R(x_(i) ⁰−x_(cm) ⁰)+x_(cm), wherex_(cm) ⁰ is a center of mass of the original shape and x_(cm) is acenter of mass of the deformed shape.
 19. The method of claim 17,further comprising: for each point x_(i) ⁰, defining a location for acorresponding point g_(i) in the goal shape as${g_{i} = {{\left( {{\beta\frac{A}{\sqrt[3]{\det(A)}}} + {\left( {1 - \beta} \right)R}} \right)\left( {x_{i}^{0} - x_{c\quad m}^{0}} \right)} + x_{c\quad m}}},$where x_(cm) ⁰ is a center of mass of the original shape, x_(cm) is acenter of mass of the deformed shape, A is a linear transformationmatrix, and β is a control parameter.
 20. The method of claim 17,further comprising: for each point x_(i) ⁰, defining a location for acorresponding point g_(i) in the goal shape as g_(i)=Ã{tilde over(q)}_(i), where Ã is a quadratic transformation matrix and {tilde over(q)}_(i) is a vector containing linear, quadratic, and mixed terms.